Robotic Skull Contouring for Facial Reconstruction—A Cadaveric Study
Bibliographic record
Abstract
BACKGROUND: Orbital trauma is a complex facial injury that can result in significant morbidity secondary to functional and cosmetic changes. Autologous calvarial bone grafting remains the gold standard in addressing orbital fractures, offering good biocompatibility and robust long-term outcomes. However, manual carving of the bone grafts is time-consuming and can increase the risk of inaccuracies that can affect cosmesis and function. OBJECTIVE: The authors set out to evaluate the accuracy and efficiency of robotic bone graft carving in the repair of orbital defects. METHODS: Orbital defects were simulated in 8 orbits. Calvarial bone was harvested and carved with the robot using virtual preoperative planning. Accuracy of the reconstruction was measured using a surface deviation map. Efficiency was measured by looking at carving time. A retrospective chart review provided operative time benchmarks for similar defects in cases of manual carving. RESULTS: Robotic carving achieved submillimeter accuracy (RMS 0.25-0.38 mm error). After multiple rounds of parametric optimization, carving time was also reduced from over 40 minutes to under 12 minutes. The average operative time after manual carving was 55 minutes. CONCLUSIONS: Robotic-assisted bone carving offers a very precise and time-efficient alternative to manual bone carving in orbital reconstruction. With further validation in the operating room, this technique may enhance the accuracy of the reconstruction and significantly increase efficiency.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".